Is AI mid-term or slowing down productivity? Job iteration under the J-curve effect

The J-curve effect refers to the short-term decline in productivity caused by factors such as adaptation costs and structural adjustments during the initial application of new technologies. Only after the deep integration of technology and industry, and the improvement of supporting systems, will explosive growth momentum be released, and the overall trend is highly consistent with the letter "J" shape. This pattern is particularly evident in the field of AI. As a general-purpose technology, it is not simply a replacement for existing processes, but a systematic reconstruction of production organization, job structure, and even skill requirements. The initial pains are difficult to avoid.

1、 The Pain at the Bottom of the J-Curve: Productivity Pressure and Job Structure Restructuring

The prediction of the Swiss Baida Research Institute is supported by multiple empirical studies. The National Bureau of Economic Research (NBER) in the United States has tracked the application of AI in the manufacturing industry and found that the initial introduction of AI by enterprises will incur high adjustment costs due to equipment investment, process transformation, and personnel diversion. Along with the increase in work in progress inventory and labor streamlining, short-term productivity will significantly decline. This pressure is not limited to the manufacturing industry. The replacement of knowledge-based positions such as text processing and primary data analysis by generative AI, as well as the impact of intelligent devices on repetitive service positions, are leading to structural unemployment in multiple industries, further suppressing short-term productivity improvements.

The imbalance of job iterations has exacerbated the transition pains. A McKinsey report shows that by 2030, generative AI may replace about 50% of global job positions, with low skilled and highly repetitive positions being the first to be affected. The new job types generated by AI, such as AI algorithm engineers, human-computer interaction designers, intelligent device operation and maintenance technicians, generally have high requirements for digital skills, leading to a polarization phenomenon of "low skill surplus and high skill shortage" in the labor market. This supply-demand mismatch not only drives up the labor costs for enterprises, but also puts some workers in a difficult situation of job transfer, slowing down the pace of productivity recovery.

2、 Key to breaking the deadlock: seeking dynamic balance in job iteration

The core of Ma Yuya's "balance strategy" lies in breaking through the closed loop of "job loss skill upgrading new job takeover". From international experience, the United States supports cutting-edge research in AI through funding from the National Science Foundation, and simultaneously promotes cooperation between community colleges and enterprises to provide short-term skills training, helping workers adapt to new positions such as AI operations and data annotation; Germany relies on a dual education system to integrate AI skills into vocational training, achieving precise alignment between industry demand and talent supply. These practices have proven that policy interventions can effectively shorten the pain cycle at the bottom of the J-curve.

As the main body of technology application, enterprises play a core role in the balancing process. By building a digital training system that covers all levels of employees, BMW Group has enabled nearly 10000 employees to master AI application skills, transforming traditional workers into innovative talents in human-machine collaboration mode. This not only reduces the impact of job loss, but also enhances organizational adaptability. 58.com and other platforms have established a universal AI skills training system, covering areas such as industrial robot operation and AI video production, to help frontline workers bridge the skills gap and achieve job transfer.

3、 Long term dividend: productivity explosion in the rising segment of the J-curve

After enduring short-term pains, the empowering effect of AI on productivity will be fully unleashed. The World Economic Forum predicts that in the next five years, the number of new jobs created by AI worldwide will reach 170 million, with a net increase of 78 million job opportunities, far exceeding the number of jobs replaced. From the perspective of industrial practice, AI has shown potential for efficiency improvement in areas such as financial risk control, medical diagnosis, and intelligent manufacturing. Olivier Ginguen é, equity partner of Swiss Baida Group, pointed out that AI can significantly narrow the productivity gap between the service and manufacturing industries, especially suitable for economies with high labor costs and slow population growth.

The realization of this long-term dividend relies on a systematic layout during the transition phase. In addition to skill training, it is also necessary to establish a dynamic monitoring mechanism for job positions, improve the unemployment security system, standardize the ethical application of AI, and avoid algorithmic discrimination that exacerbates employment inequality. The government, enterprises, and social institutions need to form a collaborative force, leaving enough space for technological innovation and providing transformation support for workers, so that the dividends of AI development can benefit all parties more fairly.